Green synthesis of <scp> MO <sub>2</sub> </scp> and Ag/ <scp> MO <sub>2</sub> </scp> (M = Ce and Ti) nanomaterials using Tulsi leaf extract and its application for <scp>CO</scp> oxidation: Insights into surface interaction
Bibliographic record
Abstract
Abstract This study investigates the catalytic performance of MO 2 and Ag/MO 2 (M = Ce and Ti) nanoparticles using green synthesis route using Tulsi ( Ocimum sanctum ) leaf extract. The role of quercetin flavonoid was confirmed from the Fourier transform infrared (FTIR) analysis of leaf extract and the final samples. X‐ray diffraction (XRD) analysis confirms the pure phase of all the samples and with the addition of silver, the crystallite size increases and lattice strain decreases. Ag/TiO 2 exhibited pronounced structural deformation and phase transformation, likely due to the interaction between Ag and TiO 2 lattice sites. Brunauer–Emmett–Teller (BET) surface area demonstrates a drop in porosity and surface area with silver addition that attributes to the nanoparticle growth according to Ostwald ripening. Scanning electron microscopy (SEM) analysis confirms the increased particle agglomeration and morphological heterogeneity, further corroborating structural modifications induced by silver incorporation. X‐ray photoelectron spectroscopy (XPS) confirmed the presence of Ce, Ti, Ag, and O in varying oxidation states, with partial oxidation of Ag in Ag/TiO 2 contributing to lattice distortions and enhanced surface reactivity. Catalytic performance evaluations demonstrated enhanced CO oxidation activity in Ag/TiO 2 ( T 50% = 360°C) and Ag/CeO 2 ( T 50% = 264°C) systems, attributed to increased oxygen vacancy concentrations, improved electron transfer, and surface reactivity. The proposed reaction mechanism for the supported catalyst is the Mars–van Krevelen mechanism. These findings highlight the importance of Ag dispersion and structural integrity in enhancing the catalytic performance of metal oxide‐supported catalysts for CO oxidation. The insights offered pathways for designing efficient and sustainable catalysts for air purification and energy conversion technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".